######## snakemake preamble start (automatically inserted, do not edit) ########
import sys; sys.path.extend(['/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/site-packages', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024', '/home/ckikawa/.conda/envs/seqneut-pipeline/bin', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/lib-dynload', '/home/ckikawa/.local/lib/python3.11/site-packages', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/site-packages', '/home/ckikawa/.cache/snakemake/snakemake/source-cache/runtime-cache/tmpy6xj203u/file/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline/notebooks', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline/notebooks']); import pickle; snakemake = 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Sequencing-based neutralization assays of 2023-2024 human serum samples versus H3N2 influenza libraries\n\nThe numerical data and computer code are at 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from snakemake.logging import logger; logger.printshellcmds = False; import os; os.chdir(r'/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024');
######## snakemake preamble end #########

Process plate counts to get fraction infectivities and fit curves

This notebook is designed to be run using snakemake, and analyzes a plate of sequencing-based neutralization assays.

The plots generated by this notebook are interactive, so you can mouseover points for details, use the mouse-scroll to zoom and pan, and use interactive dropdowns at the bottom of the plots.

Setup

Import Python modules:

import pickle
import sys

import altair as alt

import matplotlib.pyplot as plt

import neutcurve

import numpy

import pandas as pd

import ruamel.yaml as yaml

_ = alt.data_transformers.disable_max_rows()

Get the variables passed by snakemake:

count_csvs = snakemake.input.count_csvs
fate_csvs = snakemake.input.fate_csvs
viral_library_csv = snakemake.input.viral_library_csv
neut_standard_set_csv = snakemake.input.neut_standard_set_csv
qc_drops_yaml = snakemake.output.qc_drops
frac_infectivity_csv = snakemake.output.frac_infectivity_csv
fits_csv = snakemake.output.fits_csv
fits_pickle = snakemake.output.fits_pickle
samples = snakemake.params.samples
plate = snakemake.wildcards.plate
plate_params = snakemake.params.plate_params

# get thresholds turning lists into tuples as needed
manual_drops = {
    filter_type: [tuple(w) if isinstance(w, list) else w for w in filter_drops]
    for (filter_type, filter_drops) in plate_params["manual_drops"].items()
}
group = plate_params["group"]
qc_thresholds = plate_params["qc_thresholds"]
curvefit_params = plate_params["curvefit_params"]
curvefit_qc = plate_params["curvefit_qc"]
curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"] = [
    tuple(w) for w in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
]

print(f"Processing {plate=}")

samples_df = pd.DataFrame(plate_params["samples"])
print(f"\nPlate has {len(samples)} samples (wells)")
assert all(
    (len(samples_df) == samples_df[c].nunique())
    for c in ["well", "sample", "sample_noplate"]
)
assert len(samples_df) == len(
    samples_df.groupby(["serum_replicate", "dilution_factor"])
)
assert len(samples) == len(count_csvs) == len(fate_csvs) == len(samples_df)

for d, key, title in [
    (manual_drops, "manual_drops", "Data manually specified to drop:"),
    (qc_thresholds, "qc_thresholds", "QC thresholds applied to data:"),
    (curvefit_params, "curvefit_params", "Curve-fitting parameters:"),
    (curvefit_qc, "curvefit_qc", "Curve-fitting QC:"),
]:
    print(f"\n{title}")
    yaml.YAML(typ="rt").dump({key: d}, stream=sys.stdout)

Processing plate='plate14'

Plate has 96 samples (wells)

Data manually specified to drop:
manual_drops:
  barcode_wells:
  - - AAGTATTGCTACACAT
    - H3
  - - CCTATAAGGCCTTACG
    - H3

QC thresholds applied to data:
qc_thresholds:
  avg_barcode_counts_per_well: 500
  min_neut_standard_frac_per_well: 0.005
  no_serum_per_viral_barcode_filters:
    min_frac: 0.0001
    max_fold_change: 4
    max_wells: 2
  per_neut_standard_barcode_filters:
    min_frac: 0.005
    max_fold_change: 4
    max_wells: 2
  min_neut_standard_count_per_well: 1000
  min_no_serum_count_per_viral_barcode_well: 100
  max_frac_infectivity_per_viral_barcode_well: 3
  min_dilutions_per_barcode_serum_replicate: 6

Curve-fitting parameters:
curvefit_params:
  frac_infectivity_ceiling: 1
  fixtop:
  - 0.6
  - 1
  fixbottom: 0
  fixslope:
  - 0.8
  - 10

Curve-fitting QC:
curvefit_qc:
  max_frac_infectivity_at_least: 0.0
  goodness_of_fit:
    min_R2: 0.5
    max_RMSD: 0.15
  serum_replicates_ignore_curvefit_qc: []
  barcode_serum_replicates_ignore_curvefit_qc: []

Set up dictionary to keep track of wells, barcodes, well-barcodes, and serum-replicates that are dropped:

qc_drops = {
    "wells": {},
    "barcodes": {},
    "barcode_wells": {},
    "barcode_serum_replicates": {},
    "serum_replicates": {},
}

assert set(manual_drops).issubset(
    qc_drops
), f"{manual_drops.keys()=}, {qc_drops.keys()}"

Statistics on barcode-parsing for each sample

Make interactive chart of the "fates" of the sequencing reads parsed for each sample on the plate.

If most sequencing reads are not "valid barcodes", this could potentially indicate some problem in the sequencing or barcode set you are parsing.

Potential fates are:

• valid barcode: barcode that matches a known virus or neutralization standard, we hope most reads are this.
• invalid barcode: a barcode with proper flanking sequences, but does not match a known virus or neutralization standard. If you have a lot of reads of this type, it is probably a good idea to look at the invalid barcode CSVs (in the ./results/barcode_invalid/ subdirectory created by the pipeline) to see what these invalid barcodes are.
• unparseable barcode: could not parse a barcode from this read as there was not a sequence of the correct length with the appropriate flanking sequence.
• invalid outer flank: if using an outer upstream or downstream region (upstream2 or downstream2 for the illuminabarcodeparser), reads that are otherwise valid except for this outer flank. Typically you would be using upstream2 if you have a plate index embedded in your primer, and reads with this classification correspond to a different index than the one for this plate.
• low quality barcode: low-quality or N nucleotides in barcode, could indicate problem with sequencing.
• failed chastity filter: reads that failed the Illumina chastity filter, if these are reported in the FASTQ (they may not be).

Also, if the number of reads per sample is very uneven, that could indicate that you did not do a good job of balancing the different samples in the Illumina sequencing.

fates = (
    pd.concat([pd.read_csv(f).assign(sample=s) for f, s in zip(fate_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .assign(
        fate_counts=lambda x: x.groupby("fate")["count"].transform("sum"),
        sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")",
    )
    .query("fate_counts > 0")[  # only keep fates with at least one count
        ["fate", "count", "well", "serum_replicate", "sample_well", "dilution_factor"]
    ]
)

assert len(fates) == len(fates.drop_duplicates())

serum_replicates = sorted(fates["serum_replicate"].unique())
sample_wells = list(
    fates.sort_values(["serum_replicate", "dilution_factor"])["sample_well"]
)


serum_selection = alt.selection_point(
    fields=["serum_replicate"],
    bind=alt.binding_select(
        options=[None] + serum_replicates,
        labels=["all"] + serum_replicates,
        name="serum",
    ),
)

fates_chart = (
    alt.Chart(fates)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X("count", scale=alt.Scale(nice=False, padding=3)),
        alt.Y(
            "sample_well",
            title=None,
            sort=sample_wells,
        ),
        alt.Color("fate", sort=sorted(fates["fate"].unique(), reverse=True)),
        alt.Order("fate", sort="descending"),
        tooltip=fates.columns.tolist(),
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=200,
        title=f"Barcode parsing for {plate}",
    )
    .configure_axis(grid=False)
)

fates_chart

Read barcode counts and apply manually specified drops

Read the counts per barcode:

# get barcode counts
counts = (
    pd.concat([pd.read_csv(c).assign(sample=s) for c, s in zip(count_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .drop(columns=["replicate", "plate", "fastq"])
    .assign(sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")")
)

# classify barcodes as viral or neut standard
barcode_class = pd.concat(
    [
        pd.read_csv(viral_library_csv)[["barcode", "strain"]].assign(
            neut_standard=False,
        ),
        pd.read_csv(neut_standard_set_csv)[["barcode"]].assign(
            neut_standard=True,
            strain=pd.NA,
        ),
    ],
    ignore_index=True,
)

# merge counts and classification of barcodes
assert set(counts["barcode"]) == set(barcode_class["barcode"])
counts = counts.merge(barcode_class, on="barcode", validate="many_to_one")
assert set(sample_wells) == set(counts["sample_well"])
assert set(serum_replicates) == set(counts["serum_replicate"])

Apply any manually specified data drops:

for filter_type, filter_drops in manual_drops.items():
    print(f"\nDropping {len(filter_drops)} {filter_type} specified in manual_drops")
    assert filter_type in qc_drops
    qc_drops[filter_type].update(
        {w: "manual_drop" for w in filter_drops if not isinstance(w, list)}
    )
    if filter_type == "barcode_wells":
        counts = counts[
            ~counts.assign(
                barcode_well=lambda x: x.apply(
                    lambda r: (r["barcode"], r["well"]), axis=1
                )
            )["barcode_well"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "barcode_serum_replicates":
        counts = counts[
            ~counts.assign(
                barcode_serum_replicate=lambda x: x.apply(
                    lambda r: (r["barcode"], r["serum_replicate"]), axis=1
                )
            )["barcode_serum_replicate"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "wells":
        counts = counts[~counts["well"].isin(qc_drops[filter_type])]
    elif filter_type == "barcodes":
        counts = counts[~counts["barcode"].isin(qc_drops[filter_type])]
    else:
        assert filter_type in set(counts.columns)
        counts = counts[~counts[filter_type].isin(qc_drops[filter_type])]

Dropping 2 barcode_wells specified in manual_drops

Average counts per barcode in each well

Plot average counts per barcode. If a sample has inadequate barcode counts, it may not have good enough statistics for accurate analysis, and a QC-threshold is applied:

avg_barcode_counts = (
    counts.groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(avg_count=pd.NamedAgg("count", "mean"))
    .assign(
        fails_qc=lambda x: (
            x["avg_count"] < qc_thresholds["avg_barcode_counts_per_well"]
        ),
    )
)

avg_barcode_counts_chart = (
    alt.Chart(avg_barcode_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "avg_count",
            title="average barcode counts per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['avg_barcode_counts_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if avg_barcode_counts[c].dtype == float else c
            for c in avg_barcode_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Average barcode counts per well for {plate}",
    )
    .configure_axis(grid=False)
)

display(avg_barcode_counts_chart)

# drop wells failing QC
avg_barcode_counts_per_well_drops = list(avg_barcode_counts.query("fails_qc")["well"])
print(
    f"\nDropping {len(avg_barcode_counts_per_well_drops)} wells for failing "
    f"{qc_thresholds['avg_barcode_counts_per_well']=}: "
    + str(avg_barcode_counts_per_well_drops)
)
qc_drops["wells"].update(
    {w: "avg_barcode_counts_per_well" for w in avg_barcode_counts_per_well_drops}
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 1 wells for failing qc_thresholds['avg_barcode_counts_per_well']=500: ['F2']

Fraction of counts from neutralization standard

Determine the fraction of counts from the neutralization standard in each sample, and make sure this fraction passess the QC threshold.

neut_standard_fracs = (
    counts.assign(
        neut_standard_count=lambda x: x["count"] * x["neut_standard"].astype(int)
    )
    .groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(
        total_count=pd.NamedAgg("count", "sum"),
        neut_standard_count=pd.NamedAgg("neut_standard_count", "sum"),
    )
    .assign(
        neut_standard_frac=lambda x: x["neut_standard_count"] / x["total_count"],
        fails_qc=lambda x: (
            x["neut_standard_frac"] < qc_thresholds["min_neut_standard_frac_per_well"]
        ),
    )
)

neut_standard_fracs_chart = (
    alt.Chart(neut_standard_fracs)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_frac",
            title="frac counts from neutralization standard per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_frac_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if neut_standard_fracs[c].dtype == float else c
            for c in neut_standard_fracs.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard fracs per well for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_fracs_chart)

# drop wells failing QC
min_neut_standard_frac_per_well_drops = list(
    neut_standard_fracs.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_frac_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_frac_per_well']=}: "
    + str(min_neut_standard_frac_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_frac_per_well"
        for w in min_neut_standard_frac_per_well_drops
    }
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['min_neut_standard_frac_per_well']=0.005: []

Consistency and minimum fractions for barcodes

We examine the fraction of counts attributable to each barcode. We do this splitting the data two ways:

1. Looking at all viral (but not neut-standard) barcodes only for the no-serum samples (wells).

2. Looking at just the neut-standard barcodes for all samples (wells).

The reasons is that if the experiment is set up perfectly, these fractions should be the same across all samples for each barcode. (We do not expect viral barcodes to have consistent fractions across no-serum samples as they will be neutralized differently depending on strain).

We plot these fractions in interactive plots (you can mouseover points and zoom) so you can identify barcodes that fail the expected consistency QC thresholds.

We also make sure the barcodes meet specified QC minimum thresholds for all samples, and flag any that do not.

barcode_selection = alt.selection_point(fields=["barcode"], on="mouseover", empty=False)

# look at all samples for neut standard barcodes, or no-serum samples for all barcodes
for is_neut_standard, df in counts.groupby("neut_standard"):
    if is_neut_standard:
        print(
            f"\n\n{'=' * 89}\nAnalyzing neut-standard barcodes from all samples (wells)"
        )
        qc_name = "per_neut_standard_barcode_filters"
    else:
        print(f"\n\n{'=' * 89}\nAnalyzing all barcodes from no-serum samples (wells)")
        qc_name = "no_serum_per_viral_barcode_filters"
        df = df.query("serum == 'none'")

    df = df.assign(
        sample_counts=lambda x: x.groupby("sample")["count"].transform("sum"),
        count_frac=lambda x: x["count"] / x["sample_counts"],
        median_count_frac=lambda x: x.groupby("barcode")["count_frac"].transform(
            "median"
        ),
        fold_change_from_median=lambda x: numpy.where(
            x["count_frac"] > x["median_count_frac"],
            x["count_frac"] / x["median_count_frac"],
            x["median_count_frac"] / x["count_frac"],
        ),
    )[
        [
            "barcode",
            "count",
            "well",
            "sample_well",
            "count_frac",
            "median_count_frac",
            "fold_change_from_median",
        ]
        + ([] if is_neut_standard else ["strain"])
    ]

    # barcode fails QC if fails in sufficient wells
    qc = qc_thresholds[qc_name]
    print(f"Apply QC {qc_name}: {qc}\n")
    fails_qc = (
        df.assign(
            fails_qc=lambda x: ~(
                (x["count_frac"] >= qc["min_frac"])
                & (x["fold_change_from_median"] <= qc["max_fold_change"])
            ),
        )
        .groupby("barcode", as_index=False)
        .aggregate(n_wells_fail_qc=pd.NamedAgg("fails_qc", "sum"))
        .assign(fails_qc=lambda x: x["n_wells_fail_qc"] >= qc["max_wells"])[
            ["barcode", "fails_qc"]
        ]
    )
    df = df.merge(fails_qc, on="barcode", validate="many_to_one")

    # make chart
    evenness_chart = (
        alt.Chart(df)
        .add_params(barcode_selection)
        .encode(
            alt.X(
                "count_frac",
                title=(
                    "barcode's fraction of neut standard counts"
                    if is_neut_standard
                    else "barcode's fraction of non-neut standard counts"
                ),
                scale=alt.Scale(nice=False, padding=5),
            ),
            alt.Y("sample_well", sort=sample_wells),
            alt.Fill(
                "fails_qc",
                title=f"fails {qc_name}",
                legend=alt.Legend(titleLimit=500),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".2g") if df[c].dtype == float else c
                for c in df.columns
            ],
        )
        .mark_circle(fillOpacity=0.45, stroke="black", strokeOpacity=1)
        .properties(
            height=alt.Step(10),
            width=300,
            title=alt.TitleParams(
                (
                    f"{plate} all samples, neut-standard barcodes"
                    if is_neut_standard
                    else f"{plate} no-serum samples, all barcodes"
                ),
                subtitle="x-axis is zoomable (use mouse scroll/pan)",
            ),
        )
        .configure_axis(grid=False)
        .configure_legend(titleLimit=1000)
        .interactive()
    )

    display(evenness_chart)

    # drop barcodes failing QC
    barcode_drops = list(fails_qc.query("fails_qc")["barcode"])
    print(
        f"\nDropping {len(barcode_drops)} barcodes for failing {qc=}: {barcode_drops}"
    )
    qc_drops["barcodes"].update(
        {bc: "min_neut_standard_frac_per_well" for bc in barcode_drops}
    )
    counts = counts[~counts["barcode"].isin(qc_drops["barcodes"])]

=========================================================================================
Analyzing all barcodes from no-serum samples (wells)
Apply QC no_serum_per_viral_barcode_filters: {'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}

Dropping 4 barcodes for failing qc={'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}: ['CCCTCCTCAAGGGTAA', 'CGTCCCTGGCGTGTCG', 'TATATGGAATACTAAA', 'TCTCCGATAGCCCTAC']


=========================================================================================
Analyzing neut-standard barcodes from all samples (wells)
Apply QC per_neut_standard_barcode_filters: {'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}

Dropping 0 barcodes for failing qc={'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}: []

Compute fraction infectivity

The fraction infectivity for viral barcode $v_b$ in sample $s$ is computed as: $$ F_{v_b,s} = \frac{c_{v_b,s} / \left(\sum_{n_b} c_{n_b,s}\right)}{{\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]} $$ where

• $c_{v_b,s}$ is the counts of viral barcode $v_b$ in sample $s$.
• $\sum_{n_b} c_{n_b,s}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for sample $s$.
• $c_{v_b,s_0}$ is the counts of viral barcode $v_b$ in no-serum sample $s_0$.
• $\sum_{n_b} c_{n_b,s_0}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for no-serum sample $s_0$.
• ${\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]$ is the median taken across all no-serum samples of the counts of viral barcode $v_b$ versus the total counts for all neutralization standard barcodes.

First, compute the total neutralization-standard counts for each sample (well). Plot these, and drop any wells that do not meet the QC threshold.

neut_standard_counts = (
    counts.query("neut_standard")
    .groupby(
        ["well", "serum_replicate", "sample_well", "dilution_factor"],
        dropna=False,
        as_index=False,
    )
    .aggregate(neut_standard_count=pd.NamedAgg("count", "sum"))
    .assign(
        fails_qc=lambda x: (
            x["neut_standard_count"] < qc_thresholds["min_neut_standard_count_per_well"]
        ),
    )
)

neut_standard_counts_chart = (
    alt.Chart(neut_standard_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_count",
            title="counts from neutralization standard",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_count_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if neut_standard_counts[c].dtype == float
                else c
            )
            for c in neut_standard_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard counts for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_counts_chart)

# drop wells failing QC
min_neut_standard_count_per_well_drops = list(
    neut_standard_counts.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_count_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_count_per_well']=}: "
    + str(min_neut_standard_count_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_count_per_well"
        for w in min_neut_standard_count_per_well_drops
    }
)
neut_standard_counts = neut_standard_counts[
    ~neut_standard_counts["well"].isin(qc_drops["wells"])
]
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['min_neut_standard_count_per_well']=1000: []

Compute and plot the no-serum sample viral barcode counts and check if they pass the QC filters.

no_serum_counts = (
    counts.query("serum == 'none'")
    .query("not neut_standard")
    .merge(neut_standard_counts, validate="many_to_one")[
        ["barcode", "strain", "well", "sample_well", "count", "neut_standard_count"]
    ]
    .assign(
        fails_qc=lambda x: (
            x["count"] <= qc_thresholds["min_no_serum_count_per_viral_barcode_well"]
        ),
    )
)

strains = sorted(no_serum_counts["strain"].unique())
strain_selection_dropdown = alt.selection_point(
    fields=["strain"],
    bind=alt.binding_select(
        options=[None] + strains,
        labels=["all"] + strains,
        name="virus strain",
    ),
)

# make chart
no_serum_counts_chart = (
    alt.Chart(no_serum_counts)
    .add_params(barcode_selection, strain_selection_dropdown)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "count", title="viral barcode count", scale=alt.Scale(nice=False, padding=5)
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
        tooltip=no_serum_counts.columns.tolist(),
    )
    .mark_circle(fillOpacity=0.6, stroke="black", strokeOpacity=1)
    .properties(
        height=alt.Step(10),
        width=400,
        title=f"{plate} viral barcode counts in no-serum samples",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .interactive()
)

display(no_serum_counts_chart)

# drop barcode / wells failing QC
min_no_serum_count_per_viral_barcode_well_drops = list(
    no_serum_counts.query("fails_qc")[["barcode", "well"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_no_serum_count_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}: "
    + str(min_no_serum_count_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "min_no_serum_count_per_viral_barcode_well"
        for w in min_no_serum_count_per_viral_barcode_well_drops
    }
)
no_serum_counts = no_serum_counts[
    ~no_serum_counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]
counts = counts[
    ~counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]

Dropping 5 barcode-wells for failing qc_thresholds['min_no_serum_count_per_viral_barcode_well']=100: [('ATAGAAAATTATCCGC', 'C12'), ('CCCGCTAACCCTGTCT', 'F12'), ('CCAATCCCAGCCTTTA', 'F12'), ('CTCCAATAGGAGACGA', 'G12'), ('CCTATAAGGCCTTACG', 'H12')]

Compute and plot the median ratio of viral barcode count to neut standard counts across no-serum samples. If library composition is equal, all of these values should be similar:

median_no_serum_ratio = (
    no_serum_counts.assign(ratio=lambda x: x["count"] / x["neut_standard_count"])
    .groupby(["barcode", "strain"], as_index=False)
    .aggregate(median_no_serum_ratio=pd.NamedAgg("ratio", "median"))
)

strain_selection = alt.selection_point(fields=["strain"], on="mouseover", empty=False)

median_no_serum_ratio_chart = (
    alt.Chart(median_no_serum_ratio)
    .add_params(strain_selection)
    .encode(
        alt.X(
            "median_no_serum_ratio",
            title="median ratio of counts",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Y(
            "barcode",
            sort=alt.SortField("median_no_serum_ratio", order="descending"),
            axis=alt.Axis(labelFontSize=5),
        ),
        color=alt.condition(strain_selection, alt.value("orange"), alt.value("gray")),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if median_no_serum_ratio[c].dtype == float
                else c
            )
            for c in median_no_serum_ratio.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(5),
        width=250,
        title=f"{plate} no-serum median ratio viral barcode to neut-standard barcode",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(median_no_serum_ratio_chart)

Compute the actual fraction infectivities. We compute both the raw fraction infectivities and the ones with the ceiling applied:

frac_infectivity = (
    counts.query("not neut_standard")
    .query("serum != 'none'")
    .merge(median_no_serum_ratio, validate="many_to_one")
    .merge(neut_standard_counts, validate="many_to_one")
    .assign(
        frac_infectivity_raw=lambda x: (
            (x["count"] / x["neut_standard_count"]) / x["median_no_serum_ratio"]
        ),
        frac_infectivity_ceiling=lambda x: x["frac_infectivity_raw"].clip(
            upper=curvefit_params["frac_infectivity_ceiling"]
        ),
        concentration=lambda x: 1 / x["dilution_factor"],
        plate_barcode=lambda x: x["plate_replicate"] + "-" + x["barcode"],
    )[
        [
            "barcode",
            "plate_barcode",
            "well",
            "strain",
            "serum",
            "serum_replicate",
            "dilution_factor",
            "concentration",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
)

assert len(
    frac_infectivity.groupby(["serum", "plate_barcode", "dilution_factor"])
) == len(frac_infectivity)
assert frac_infectivity["dilution_factor"].notnull().all()
assert frac_infectivity["frac_infectivity_raw"].notnull().all()
assert frac_infectivity["frac_infectivity_ceiling"].notnull().all()

Plot the fraction infectivities, both the raw values and with the ceiling applied:

frac_infectivity_chart_df = (
    frac_infectivity.assign(
        fails_qc=lambda x: (
            x["frac_infectivity_raw"]
            > qc_thresholds["max_frac_infectivity_per_viral_barcode_well"]
        ),
    )
    .melt(
        id_vars=[
            "barcode",
            "strain",
            "well",
            "serum_replicate",
            "dilution_factor",
            "fails_qc",
        ],
        value_vars=["frac_infectivity_raw", "frac_infectivity_ceiling"],
        var_name="ceiling_applied",
        value_name="frac_infectivity",
    )
    .assign(
        ceiling_applied=lambda x: x["ceiling_applied"].map(
            {
                "frac_infectivity_raw": "raw fraction infectivity",
                "frac_infectivity_ceiling": f"fraction infectivity with ceiling at {curvefit_params['frac_infectivity_ceiling']}",
            }
        )
    )
)

frac_infectivity_chart = (
    alt.Chart(frac_infectivity_chart_df)
    .add_params(strain_selection_dropdown, barcode_selection)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "dilution_factor",
            title="dilution factor",
            scale=alt.Scale(nice=False, padding=5, type="log"),
        ),
        alt.Y(
            "frac_infectivity",
            title="fraction infectivity",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Column(
            "ceiling_applied",
            sort="descending",
            title=None,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold", labelPadding=2),
        ),
        alt.Row(
            "serum_replicate",
            title=None,
            spacing=3,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold"),
        ),
        alt.Detail("barcode"),
        alt.Shape(
            "fails_qc",
            title=f"fails {qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        color=alt.condition(
            barcode_selection, alt.value("black"), alt.value("MediumBlue")
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(3), alt.value(1)),
        opacity=alt.condition(barcode_selection, alt.value(1), alt.value(0.25)),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if frac_infectivity_chart_df[c].dtype == float
                else c
            )
            for c in frac_infectivity_chart_df.columns
        ],
    )
    .mark_line(point=True)
    .properties(
        height=150,
        width=250,
        title=f"Fraction infectivities for {plate}",
    )
    .interactive(bind_x=False)
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .configure_point(size=50)
    .resolve_scale(x="independent", y="independent")
)

display(frac_infectivity_chart)

# drop barcode / wells failing QC
max_frac_infectivity_per_viral_barcode_well_drops = list(
    frac_infectivity_chart_df.query("fails_qc")[["barcode", "well"]]
    .drop_duplicates()
    .itertuples(index=False, name=None)
)
print(
    f"\nDropping {len(max_frac_infectivity_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}: "
    + str(max_frac_infectivity_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "max_frac_infectivity_per_viral_barcode_well"
        for w in max_frac_infectivity_per_viral_barcode_well_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]

Dropping 38 barcode-wells for failing qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=3: [('CCAATCCCAGCCTTTA', 'B6'), ('CAATTCGCCGTTCCCC', 'E6'), ('CCAATCCCAGCCTTTA', 'E6'), ('TGCCGATCCAATTGAT', 'F6'), ('CCAATCCCAGCCTTTA', 'H6'), ('CAATTCGCCGTTCCCC', 'D7'), ('CAATTCGCCGTTCCCC', 'E7'), ('ATTAGATTATAACGTA', 'A8'), ('AATCTTTCCAATCTTG', 'A8'), ('CGAAAACATTACAAAT', 'F8'), ('CAATTCGCCGTTCCCC', 'G8'), ('AATGACAGCTGTCTAG', 'B9'), ('CTCATTACAGAAATTG', 'C9'), ('CCAATCCCAGCCTTTA', 'C9'), ('CGTACAGTGTAATCGA', 'D9'), ('AGCTATGCCTAGTGAA', 'D9'), ('AACCACCCCAGAGATG', 'C10'), ('ATAGAAAATTATCCGC', 'C10'), ('CATAATGCACAAACGC', 'D10'), ('TGCCGATCCAATTGAT', 'E10'), ('TCTTAGAGTGAACGAT', 'F10'), ('CAATTCGCCGTTCCCC', 'G10'), ('CTCCAATAGGAGACGA', 'G10'), ('GACCAAAGCTGCAGGG', 'H10'), ('AACCACCCCAGAGATG', 'B11'), ('GCATTATAATCTTGTG', 'C11'), ('CGTACAGTGTAATCGA', 'C11'), ('ATAGAAAATTATCCGC', 'C11'), ('CAATTCGCCGTTCCCC', 'C11'), ('CCAATCCCAGCCTTTA', 'D11'), ('CTCCAATAGGAGACGA', 'D11'), ('CTCATTACAGAAATTG', 'F11'), ('CAATTCGCCGTTCCCC', 'F11'), ('TGCCGATCCAATTGAT', 'G11'), ('CAATTCGCCGTTCCCC', 'G11'), ('AACCACCCCAGAGATG', 'G11'), ('AATGACAGCTGTCTAG', 'G11'), ('CGAAAACATTACAAAT', 'H11')]

Check how many dilutions we have per barcode / serum-replicate:

n_dilutions = (
    frac_infectivity.groupby(["serum_replicate", "strain", "barcode"], as_index=False)
    .aggregate(**{"number of dilutions": pd.NamedAgg("dilution_factor", "nunique")})
    .assign(
        fails_qc=lambda x: (
            x["number of dilutions"]
            < qc_thresholds["min_dilutions_per_barcode_serum_replicate"]
        ),
    )
)

n_dilutions_chart = (
    alt.Chart(n_dilutions)
    .add_params(barcode_selection)
    .encode(
        alt.X("number of dilutions", scale=alt.Scale(nice=False, padding=4)),
        alt.Y("strain", title=None),
        alt.Column(
            "serum_replicate",
            title=None,
            header=alt.Header(labelFontSize=12, labelFontStyle="bold", labelPadding=0),
        ),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
        tooltip=[
            alt.Tooltip(c, format=".3g") if n_dilutions[c].dtype == float else c
            for c in n_dilutions.columns
        ],
    )
    .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.45)
    .properties(
        height=alt.Step(10),
        width=120,
        title=alt.TitleParams(
            "number of dilutions for each barcode for each serum-replicate", dy=-2
        ),
    )
)

display(n_dilutions_chart)

# drop barcode / serum-replicates failing QC
min_dilutions_per_barcode_serum_replicate_drops = list(
    n_dilutions.query("fails_qc")[["barcode", "serum_replicate"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_dilutions_per_barcode_serum_replicate_drops)} barcode/serum-replicates for failing "
    f"{qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}: "
    + str(min_dilutions_per_barcode_serum_replicate_drops)
)
qc_drops["barcode_serum_replicates"].update(
    {
        w: "min_dilutions_per_barcode_serum_replicate"
        for w in min_dilutions_per_barcode_serum_replicate_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_serum_replicate=lambda x: x.apply(
            lambda r: (r["barcode"], r["serum_replicate"]), axis=1
        )
    )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
]

Dropping 0 barcode/serum-replicates for failing qc_thresholds['min_dilutions_per_barcode_serum_replicate']=6: []

Fit neutralization curves without applying QC to curves

First fit curves to all serum replicates, then we will apply QC on the curve fits. Note that the fitting is done to the fraction infectivities with the ceiling:

fits_noqc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum_replicate",
    replicate_col="barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

Determine which fits fail the curve fitting QC, and plot them. Note the plot indicates as failing QC any barcode / serum-replicate that fails, even if we are also specified to ignore the QC for that one (so it will not be removed later):

goodness_of_fit = curvefit_qc["goodness_of_fit"]

fit_params_noqc = (
    frac_infectivity.groupby(["serum_replicate", "barcode"], as_index=False)
    .aggregate(max_frac_infectivity=pd.NamedAgg("frac_infectivity_ceiling", "max"))
    .merge(
        fits_noqc.fitParams(average_only=False, no_average=True)[
            ["serum", "virus", "replicate", "r2", "rmsd"]
        ].rename(columns={"serum": "serum_replicate", "replicate": "barcode"}),
        validate="one_to_one",
    )
    .assign(
        fails_max_frac_infectivity_at_least=lambda x: (
            x["max_frac_infectivity"] < curvefit_qc["max_frac_infectivity_at_least"]
        ),
        fails_goodness_of_fit=lambda x: (
            (x["r2"] < goodness_of_fit["min_R2"])
            & (x["rmsd"] > goodness_of_fit["max_RMSD"])
        ),
        fails_qc=lambda x: (
            x["fails_max_frac_infectivity_at_least"] | x["fails_goodness_of_fit"]
        ),
        ignore_qc=lambda x: x.apply(
            lambda r: (
                (
                    r["serum_replicate"]
                    in curvefit_qc["serum_replicates_ignore_curvefit_qc"]
                )
                or (
                    (r["barcode"], r["serum_replicate"])
                    in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
                )
            ),
            axis=1,
        ),
    )
)

print(f"Plotting barcode / serum-replicates that fail {curvefit_qc=}\n")

for prop, col in [
    ("max frac infectivity", "max_frac_infectivity"),
    ("curve fit R2", "r2"),
    ("curve fit RMSD", "rmsd"),
]:
    fit_params_noqc_chart = (
        alt.Chart(fit_params_noqc)
        .add_params(barcode_selection)
        .encode(
            alt.X(col, title=prop, scale=alt.Scale(nice=False, padding=4)),
            alt.Y("virus", title=None),
            alt.Fill("fails_qc"),
            alt.Column(
                "serum_replicate",
                title=None,
                header=alt.Header(
                    labelFontSize=12, labelFontStyle="bold", labelPadding=0
                ),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".3g") if fit_params_noqc[c].dtype == float else c
                for c in fit_params_noqc.columns
            ],
        )
        .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.55)
        .properties(
            height=alt.Step(10),
            width=120,
            title=alt.TitleParams(f"{prop} for each barcode serum-replicate", dy=-2),
        )
    )
    display(fit_params_noqc_chart)

Plotting barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}

Now get all barcode / serum-replicate pairs that fail any of the QC. Plot curves for just these virus / serum-replicates (we plot all barcodes for a virus even if just one fails QC), and then exclude any that are not specified to ignore the QC:

barcode_serum_replicates_fail_qc = fit_params_noqc.query("fails_qc").reset_index(
    drop=True
)
print(f"Here are barcode / serum-replicates that fail {curvefit_qc=}")
display(barcode_serum_replicates_fail_qc)

if len(barcode_serum_replicates_fail_qc):
    print("\nCurves for viruses and serum-replicates with at least one failed barcode:")
    fig, _ = fits_noqc.plotReplicates(
        sera=sorted(barcode_serum_replicates_fail_qc["serum_replicate"].unique()),
        viruses=sorted(barcode_serum_replicates_fail_qc["virus"].unique()),
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=10,
        ticksize=10,
        ncol=6,
        draw_in_bounds=True,
    )
    display(fig)
    plt.close(fig)

# drop barcode / serum-replicates failing QC
for qc_filter in ["max_frac_infectivity_at_least", "goodness_of_fit"]:
    fits_qc_drops = list(
        fit_params_noqc.query(f"fails_{qc_filter} and (not ignore_qc)")[
            ["barcode", "serum_replicate"]
        ].itertuples(index=False, name=None)
    )
    print(
        f"\nDropping {len(fits_qc_drops)} barcode/serum-replicates for failing "
        f"{qc_filter}={curvefit_qc[qc_filter]}: " + str(fits_qc_drops)
    )
    qc_drops["barcode_serum_replicates"].update({w: qc_filter for w in fits_qc_drops})
    frac_infectivity = frac_infectivity[
        ~frac_infectivity.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]
    fit_params_noqc = fit_params_noqc[
        ~fit_params_noqc.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]

Here are barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}

	serum_replicate	barcode	max_frac_infectivity	virus	r2	rmsd	fails_max_frac_infectivity_at_least	fails_goodness_of_fit	fails_qc	ignore_qc
0	PENN23_y1993_s020_d0	AAACTTCGTGGTATAC	0.961296	A/Solwezi/13-NIC-001/2023	0.470425	0.197456	False	True	True	False
1	PENN23_y1993_s020_d0	AAAGACCTTTAACTCT	1.000000	A/Singapore/INFIMH-16-0019/2016_X-307A	0.371097	0.256201	False	True	True	False
2	PENN23_y1993_s020_d0	AACAAGGCCAACATTT	0.829214	A/Netherlands/01760/2023	0.293715	0.211207	False	True	True	False
3	PENN23_y1993_s020_d0	AACAATTAATTTTTCA	1.000000	A/Bhutan/0845/2023	0.392644	0.208465	False	True	True	False
4	PENN23_y1993_s020_d0	AAGCTAATCGTAGTCC	0.940313	A/Maldives/852/2023	0.483179	0.243229	False	True	True	False
5	PENN23_y1993_s020_d0	AGTGCTATAAAAATCA	1.000000	A/South_Africa/R07188/2023	0.256266	0.250961	False	True	True	False
6	PENN23_y1993_s020_d0	AGTTATGTAAAACGTG	0.736595	A/South_Africa/R06359/2023	0.357399	0.204416	False	True	True	False
7	PENN23_y1993_s020_d0	CAAAAAGCTAATAAGT	0.806653	A/Bhutan/0006/2023	0.480188	0.176519	False	True	True	False
8	PENN23_y1993_s020_d0	CCATCACCTTATACAC	0.584844	A/Netherlands/01693/2023	0.125985	0.174047	False	True	True	False
9	PENN23_y1993_s020_d0	CCTATAAGGCCTTACG	1.000000	A/Wisconsin/27/2023	0.483921	0.206414	False	True	True	False
10	PENN23_y1993_s020_d0	CGCAAGGGATACTAAC	1.000000	A/Bangkok/P3755/2023	0.478451	0.220279	False	True	True	False
11	PENN23_y1993_s020_d0	CGGGAATCTCCCATAC	0.675684	A/Hong_Kong/2671/2019	0.384996	0.173167	False	True	True	False
12	PENN23_y1993_s020_d0	CTCCAATAGGAGACGA	1.000000	A/Brisbane/429/2023	0.362725	0.287854	False	True	True	False
13	PENN23_y1993_s020_d0	CTGCGAATATTGTGAC	0.612720	A/SENDAI/45/2023	0.378927	0.169299	False	True	True	False
14	PENN23_y1993_s020_d0	GACCAAAAAGCAGTAT	1.000000	A/Saskatchewan/SKFLU317847/2023	0.445593	0.207246	False	True	True	False
15	PENN23_y1993_s020_d0	GGGTGCAATGAATCCA	0.927184	A/Maldives/852/2023	0.473003	0.223307	False	True	True	False
16	PENN23_y1993_s020_d0	TAGATAATAAGATTCA	1.000000	A/Catalonia/2041146NS/2023	0.493043	0.189991	False	True	True	False
17	PENN23_y1993_s020_d0	TATCGCAATATGATAA	0.792838	A/Abu_Dhabi/6753/2023	0.449170	0.193924	False	True	True	False
18	PENN23_y1993_s020_d0	TCCCGAACTGAACGCG	0.631571	A/Townsville/68/2023	0.368300	0.151117	False	True	True	False
19	PENN23_y1993_s020_d0	TCCGCCACTATAACAT	0.860545	A/South_Africa/R05510/2023	0.392759	0.185392	False	True	True	False
20	PENN23_y1993_s020_d0	TCGCGGTAGATTTGCG	0.683927	A/Bhutan/FLU-BTG-00988/2022	0.395024	0.180966	False	True	True	False
21	PENN23_y1993_s020_d0	TCTTGAATTTCATGGA	0.817162	A/Abu_Dhabi/6753/2023	0.495166	0.173674	False	True	True	False
22	PENN23_y1993_s020_d0	TGACAAACACCTGAGG	0.570243	A/SENDAI/45/2023	-0.003182	0.164696	False	True	True	False
23	PENN23_y1993_s020_d0	TTGCAATTGAAACATA	1.000000	A/Cambodia/e0826360/2020	0.486692	0.234758	False	True	True	False
24	PENN23_y1993_s020_d0	TTGCTAGTCTACCTGA	0.959654	A/Brisbane/429/2023	0.386262	0.201363	False	True	True	False
25	PENN23_y1993_s022_d0	ACGCAAATAGACCGAA	0.706442	A/Texas/50/2012_X-223A_(13/252)	0.240162	0.189325	False	True	True	False
26	PENN23_y1993_s022_d0	CAAAAAGCTAATAAGT	1.000000	A/Bhutan/0006/2023	0.482496	0.227057	False	True	True	False
27	PENN23_y1993_s022_d0	CACCGACCAACTCTCT	1.000000	A/South_Africa/R06240/2023	0.413285	0.274083	False	True	True	False
28	PENN23_y1993_s022_d0	CCCGCTAACCCTGTCT	0.805108	A/YAMAGATA/98/2023	0.176696	0.255259	False	True	True	False
29	PENN23_y1993_s022_d0	CGGCTAAAGTCTATAG	1.000000	A/South_Africa/R07073/2023	0.445840	0.291057	False	True	True	False
30	PENN23_y1993_s022_d0	GCTGGTGCACAAGATT	1.000000	A/Abu_Dhabi/6753/2023	0.418552	0.224890	False	True	True	False
31	PENN23_y1993_s022_d0	TTACATTTTTAGAATT	0.649503	A/Bangkok/P3599/2023	0.335745	0.187950	False	True	True	False
32	PENN23_y1993_s022_d0	TTGACTCACCGAATAA	1.000000	A/Cambodia/e0826360/2020	0.392239	0.240936	False	True	True	False
33	PENN23_y1993_s022_d28	ACAAAGATAAAAATTT	1.000000	A/Guangdong-Futian/1980/2023	0.370691	0.281266	False	True	True	False
34	PENN23_y1993_s022_d28	ACGCAAATAGACCGAA	1.000000	A/Texas/50/2012_X-223A_(13/252)	0.461758	0.222375	False	True	True	False
35	PENN23_y1993_s022_d28	AGACCATCGCACCCAA	1.000000	A/Thailand/8/2022	0.481036	0.220173	False	True	True	False
36	PENN23_y1993_s022_d28	AGCTATGCCTAGTGAA	1.000000	A/Finland/391/2023	0.292746	0.285932	False	True	True	False
37	PENN23_y1993_s022_d28	ATGGTTATCTTACCTT	1.000000	A/Luga/RII-11393S/2023	0.399368	0.240246	False	True	True	False
38	PENN23_y1993_s022_d28	TTGACTCACCGAATAA	1.000000	A/Cambodia/e0826360/2020	0.401865	0.245934	False	True	True	False
39	PENN23_y1994_s021_d0	ATGCGTCTAAACATAG	1.000000	A/Guangdong-Futian/1980/2023	0.422175	0.266679	False	True	True	False
40	PENN23_y1994_s021_d0	CATAATGCACAAACGC	1.000000	A/South_Africa/R06359/2023	0.347496	0.310885	False	True	True	False
41	PENN23_y1994_s021_d0	CCCGCTAACCCTGTCT	0.615881	A/YAMAGATA/98/2023	0.164374	0.163977	False	True	True	False
42	PENN23_y1994_s021_d0	CCGCAATGACAATTTG	1.000000	A/Finland/391/2023	0.498200	0.265480	False	True	True	False
43	PENN23_y1994_s021_d28	CCAATCCCAGCCTTTA	1.000000	A/Switzerland/9715293/2013	0.441308	0.259908	False	True	True	False
44	PENN23_y1997_s023_d0	ATTTTTCTATGGCTAC	1.000000	A/Hong_Kong/4801/2014	0.369433	0.335023	False	True	True	False
45	PENN23_y1997_s023_d28	AAGTATTGCTACACAT	1.000000	A/Hong_Kong/4801/2014	0.452860	0.306850	False	True	True	False
46	PENN23_y1997_s023_d28	CTCCAATAGGAGACGA	1.000000	A/Brisbane/429/2023	0.465712	0.304051	False	True	True	False
47	PENN23_y1997_s023_d28	TTAACCTAACGTATAG	1.000000	A/Brisbane/429/2023	0.443648	0.290342	False	True	True	False

Curves for viruses and serum-replicates with at least one failed barcode:

Dropping 0 barcode/serum-replicates for failing max_frac_infectivity_at_least=0.0: []

Dropping 48 barcode/serum-replicates for failing goodness_of_fit={'min_R2': 0.5, 'max_RMSD': 0.15}: [('AAACTTCGTGGTATAC', 'PENN23_y1993_s020_d0'), ('AAAGACCTTTAACTCT', 'PENN23_y1993_s020_d0'), ('AACAAGGCCAACATTT', 'PENN23_y1993_s020_d0'), ('AACAATTAATTTTTCA', 'PENN23_y1993_s020_d0'), ('AAGCTAATCGTAGTCC', 'PENN23_y1993_s020_d0'), ('AGTGCTATAAAAATCA', 'PENN23_y1993_s020_d0'), ('AGTTATGTAAAACGTG', 'PENN23_y1993_s020_d0'), ('CAAAAAGCTAATAAGT', 'PENN23_y1993_s020_d0'), ('CCATCACCTTATACAC', 'PENN23_y1993_s020_d0'), ('CCTATAAGGCCTTACG', 'PENN23_y1993_s020_d0'), ('CGCAAGGGATACTAAC', 'PENN23_y1993_s020_d0'), ('CGGGAATCTCCCATAC', 'PENN23_y1993_s020_d0'), ('CTCCAATAGGAGACGA', 'PENN23_y1993_s020_d0'), ('CTGCGAATATTGTGAC', 'PENN23_y1993_s020_d0'), ('GACCAAAAAGCAGTAT', 'PENN23_y1993_s020_d0'), ('GGGTGCAATGAATCCA', 'PENN23_y1993_s020_d0'), ('TAGATAATAAGATTCA', 'PENN23_y1993_s020_d0'), ('TATCGCAATATGATAA', 'PENN23_y1993_s020_d0'), ('TCCCGAACTGAACGCG', 'PENN23_y1993_s020_d0'), ('TCCGCCACTATAACAT', 'PENN23_y1993_s020_d0'), ('TCGCGGTAGATTTGCG', 'PENN23_y1993_s020_d0'), ('TCTTGAATTTCATGGA', 'PENN23_y1993_s020_d0'), ('TGACAAACACCTGAGG', 'PENN23_y1993_s020_d0'), ('TTGCAATTGAAACATA', 'PENN23_y1993_s020_d0'), ('TTGCTAGTCTACCTGA', 'PENN23_y1993_s020_d0'), ('ACGCAAATAGACCGAA', 'PENN23_y1993_s022_d0'), ('CAAAAAGCTAATAAGT', 'PENN23_y1993_s022_d0'), ('CACCGACCAACTCTCT', 'PENN23_y1993_s022_d0'), ('CCCGCTAACCCTGTCT', 'PENN23_y1993_s022_d0'), ('CGGCTAAAGTCTATAG', 'PENN23_y1993_s022_d0'), ('GCTGGTGCACAAGATT', 'PENN23_y1993_s022_d0'), ('TTACATTTTTAGAATT', 'PENN23_y1993_s022_d0'), ('TTGACTCACCGAATAA', 'PENN23_y1993_s022_d0'), ('ACAAAGATAAAAATTT', 'PENN23_y1993_s022_d28'), ('ACGCAAATAGACCGAA', 'PENN23_y1993_s022_d28'), ('AGACCATCGCACCCAA', 'PENN23_y1993_s022_d28'), ('AGCTATGCCTAGTGAA', 'PENN23_y1993_s022_d28'), ('ATGGTTATCTTACCTT', 'PENN23_y1993_s022_d28'), ('TTGACTCACCGAATAA', 'PENN23_y1993_s022_d28'), ('ATGCGTCTAAACATAG', 'PENN23_y1994_s021_d0'), ('CATAATGCACAAACGC', 'PENN23_y1994_s021_d0'), ('CCCGCTAACCCTGTCT', 'PENN23_y1994_s021_d0'), ('CCGCAATGACAATTTG', 'PENN23_y1994_s021_d0'), ('CCAATCCCAGCCTTTA', 'PENN23_y1994_s021_d28'), ('ATTTTTCTATGGCTAC', 'PENN23_y1997_s023_d0'), ('AAGTATTGCTACACAT', 'PENN23_y1997_s023_d28'), ('CTCCAATAGGAGACGA', 'PENN23_y1997_s023_d28'), ('TTAACCTAACGTATAG', 'PENN23_y1997_s023_d28')]

Fit neutralization curves after applying QC

No we re-fit curves after applying all the QC:

fits_qc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum",
    replicate_col="plate_barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

fit_params_qc = fits_qc.fitParams(average_only=False, no_average=True)
assert len(fit_params_qc) <= len(
    fits_noqc.fitParams(average_only=False, no_average=True)
)

print(f"Assigning fits for this plate to {group}")
fit_params_qc.insert(0, "group", group)

Assigning fits for this plate to platesToRepeat

Plot all the curves that passed QC:

if fits_qc.sera:
    _ = fits_qc.plotReplicates(
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=10,
        ticksize=10,
        ncol=6,
        draw_in_bounds=True,
    )
else:
    print("No sera passed QC.")

Save results to files

print(f"Writing fraction infectivities to {frac_infectivity_csv}")
(
    frac_infectivity[
        [
            "serum",
            "strain",
            "plate_barcode",
            "dilution_factor",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
    .sort_values(["serum", "plate_barcode", "dilution_factor"])
    .to_csv(frac_infectivity_csv, index=False, float_format="%.4g")
)

print(f"\nWriting fit parameters to {fits_csv}")
(
    fit_params_qc.drop(columns=["nreplicates", "ic50_str"]).to_csv(
        fits_csv, index=False, float_format="%.4g"
    )
)

print(f"\nPickling neutcurve.CurveFits object for these data to {fits_pickle}")
with open(fits_pickle, "wb") as f:
    pickle.dump(fits_qc, f)

print(f"\nWriting QC drops to {qc_drops_yaml}")


def tup_to_str(x):
    return " ".join(x) if isinstance(x, tuple) else x


qc_drops_for_yaml = {
    key: {tup_to_str(key2): val2 for key2, val2 in val.items()}
    for key, val in qc_drops.items()
}
with open(qc_drops_yaml, "w") as f:
    yaml.YAML(typ="rt").dump(qc_drops_for_yaml, f)
print("\nHere are the QC drops:\n***************************")
yaml.YAML(typ="rt").dump(qc_drops_for_yaml, sys.stdout)

Writing fraction infectivities to results/plates/plate14/frac_infectivity.csv

Writing fit parameters to results/plates/plate14/curvefits.csv

Pickling neutcurve.CurveFits object for these data to results/plates/plate14/curvefits.pickle

Writing QC drops to results/plates/plate14/qc_drops.yml

Here are the QC drops:
***************************
wells:
  F2: avg_barcode_counts_per_well
barcodes:
  CCCTCCTCAAGGGTAA: min_neut_standard_frac_per_well
  CGTCCCTGGCGTGTCG: min_neut_standard_frac_per_well
  TATATGGAATACTAAA: min_neut_standard_frac_per_well
  TCTCCGATAGCCCTAC: min_neut_standard_frac_per_well
barcode_wells:
  AAGTATTGCTACACAT H3: manual_drop
  CCTATAAGGCCTTACG H3: manual_drop
  ATAGAAAATTATCCGC C12: min_no_serum_count_per_viral_barcode_well
  CCCGCTAACCCTGTCT F12: min_no_serum_count_per_viral_barcode_well
  CCAATCCCAGCCTTTA F12: min_no_serum_count_per_viral_barcode_well
  CTCCAATAGGAGACGA G12: min_no_serum_count_per_viral_barcode_well
  CCTATAAGGCCTTACG H12: min_no_serum_count_per_viral_barcode_well
  CCAATCCCAGCCTTTA B6: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC E6: max_frac_infectivity_per_viral_barcode_well
  CCAATCCCAGCCTTTA E6: max_frac_infectivity_per_viral_barcode_well
  TGCCGATCCAATTGAT F6: max_frac_infectivity_per_viral_barcode_well
  CCAATCCCAGCCTTTA H6: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC D7: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC E7: max_frac_infectivity_per_viral_barcode_well
  ATTAGATTATAACGTA A8: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG A8: max_frac_infectivity_per_viral_barcode_well
  CGAAAACATTACAAAT F8: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC G8: max_frac_infectivity_per_viral_barcode_well
  AATGACAGCTGTCTAG B9: max_frac_infectivity_per_viral_barcode_well
  CTCATTACAGAAATTG C9: max_frac_infectivity_per_viral_barcode_well
  CCAATCCCAGCCTTTA C9: max_frac_infectivity_per_viral_barcode_well
  CGTACAGTGTAATCGA D9: max_frac_infectivity_per_viral_barcode_well
  AGCTATGCCTAGTGAA D9: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG C10: max_frac_infectivity_per_viral_barcode_well
  ATAGAAAATTATCCGC C10: max_frac_infectivity_per_viral_barcode_well
  CATAATGCACAAACGC D10: max_frac_infectivity_per_viral_barcode_well
  TGCCGATCCAATTGAT E10: max_frac_infectivity_per_viral_barcode_well
  TCTTAGAGTGAACGAT F10: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC G10: max_frac_infectivity_per_viral_barcode_well
  CTCCAATAGGAGACGA G10: max_frac_infectivity_per_viral_barcode_well
  GACCAAAGCTGCAGGG H10: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG B11: max_frac_infectivity_per_viral_barcode_well
  GCATTATAATCTTGTG C11: max_frac_infectivity_per_viral_barcode_well
  CGTACAGTGTAATCGA C11: max_frac_infectivity_per_viral_barcode_well
  ATAGAAAATTATCCGC C11: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC C11: max_frac_infectivity_per_viral_barcode_well
  CCAATCCCAGCCTTTA D11: max_frac_infectivity_per_viral_barcode_well
  CTCCAATAGGAGACGA D11: max_frac_infectivity_per_viral_barcode_well
  CTCATTACAGAAATTG F11: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC F11: max_frac_infectivity_per_viral_barcode_well
  TGCCGATCCAATTGAT G11: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC G11: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG G11: max_frac_infectivity_per_viral_barcode_well
  AATGACAGCTGTCTAG G11: max_frac_infectivity_per_viral_barcode_well
  CGAAAACATTACAAAT H11: max_frac_infectivity_per_viral_barcode_well
barcode_serum_replicates:
  AAACTTCGTGGTATAC PENN23_y1993_s020_d0: goodness_of_fit
  AAAGACCTTTAACTCT PENN23_y1993_s020_d0: goodness_of_fit
  AACAAGGCCAACATTT PENN23_y1993_s020_d0: goodness_of_fit
  AACAATTAATTTTTCA PENN23_y1993_s020_d0: goodness_of_fit
  AAGCTAATCGTAGTCC PENN23_y1993_s020_d0: goodness_of_fit
  AGTGCTATAAAAATCA PENN23_y1993_s020_d0: goodness_of_fit
  AGTTATGTAAAACGTG PENN23_y1993_s020_d0: goodness_of_fit
  CAAAAAGCTAATAAGT PENN23_y1993_s020_d0: goodness_of_fit
  CCATCACCTTATACAC PENN23_y1993_s020_d0: goodness_of_fit
  CCTATAAGGCCTTACG PENN23_y1993_s020_d0: goodness_of_fit
  CGCAAGGGATACTAAC PENN23_y1993_s020_d0: goodness_of_fit
  CGGGAATCTCCCATAC PENN23_y1993_s020_d0: goodness_of_fit
  CTCCAATAGGAGACGA PENN23_y1993_s020_d0: goodness_of_fit
  CTGCGAATATTGTGAC PENN23_y1993_s020_d0: goodness_of_fit
  GACCAAAAAGCAGTAT PENN23_y1993_s020_d0: goodness_of_fit
  GGGTGCAATGAATCCA PENN23_y1993_s020_d0: goodness_of_fit
  TAGATAATAAGATTCA PENN23_y1993_s020_d0: goodness_of_fit
  TATCGCAATATGATAA PENN23_y1993_s020_d0: goodness_of_fit
  TCCCGAACTGAACGCG PENN23_y1993_s020_d0: goodness_of_fit
  TCCGCCACTATAACAT PENN23_y1993_s020_d0: goodness_of_fit
  TCGCGGTAGATTTGCG PENN23_y1993_s020_d0: goodness_of_fit
  TCTTGAATTTCATGGA PENN23_y1993_s020_d0: goodness_of_fit
  TGACAAACACCTGAGG PENN23_y1993_s020_d0: goodness_of_fit
  TTGCAATTGAAACATA PENN23_y1993_s020_d0: goodness_of_fit
  TTGCTAGTCTACCTGA PENN23_y1993_s020_d0: goodness_of_fit
  ACGCAAATAGACCGAA PENN23_y1993_s022_d0: goodness_of_fit
  CAAAAAGCTAATAAGT PENN23_y1993_s022_d0: goodness_of_fit
  CACCGACCAACTCTCT PENN23_y1993_s022_d0: goodness_of_fit
  CCCGCTAACCCTGTCT PENN23_y1993_s022_d0: goodness_of_fit
  CGGCTAAAGTCTATAG PENN23_y1993_s022_d0: goodness_of_fit
  GCTGGTGCACAAGATT PENN23_y1993_s022_d0: goodness_of_fit
  TTACATTTTTAGAATT PENN23_y1993_s022_d0: goodness_of_fit
  TTGACTCACCGAATAA PENN23_y1993_s022_d0: goodness_of_fit
  ACAAAGATAAAAATTT PENN23_y1993_s022_d28: goodness_of_fit
  ACGCAAATAGACCGAA PENN23_y1993_s022_d28: goodness_of_fit
  AGACCATCGCACCCAA PENN23_y1993_s022_d28: goodness_of_fit
  AGCTATGCCTAGTGAA PENN23_y1993_s022_d28: goodness_of_fit
  ATGGTTATCTTACCTT PENN23_y1993_s022_d28: goodness_of_fit
  TTGACTCACCGAATAA PENN23_y1993_s022_d28: goodness_of_fit
  ATGCGTCTAAACATAG PENN23_y1994_s021_d0: goodness_of_fit
  CATAATGCACAAACGC PENN23_y1994_s021_d0: goodness_of_fit
  CCCGCTAACCCTGTCT PENN23_y1994_s021_d0: goodness_of_fit
  CCGCAATGACAATTTG PENN23_y1994_s021_d0: goodness_of_fit
  CCAATCCCAGCCTTTA PENN23_y1994_s021_d28: goodness_of_fit
  ATTTTTCTATGGCTAC PENN23_y1997_s023_d0: goodness_of_fit
  AAGTATTGCTACACAT PENN23_y1997_s023_d28: goodness_of_fit
  CTCCAATAGGAGACGA PENN23_y1997_s023_d28: goodness_of_fit
  TTAACCTAACGTATAG PENN23_y1997_s023_d28: goodness_of_fit
serum_replicates: {}